Additive Random Effects Research Articles

Integrating Significant SNPs Identified by GWAS for Genomic Prediction of the Number of Ribs and Carcass Length in Suhuai Pigs

The number of ribs (NRs) and the carcass length (CL) are important economic traits. The traits are usually measured after slaughter. To improve the prediction performance of genomic selection (GS) for NRs and CL, one strategy is to integrate the significant loci identified from whole-genome sequencing (WGS) data by genome-wide association study (GWAS) into the genomic prediction (GP) model. This study investigated the GP of different genomic best linear unbiased prediction (GBLUP) and Bayesian models using chip genotype data, imputed WGS (iWGS) data and modeling significant single-nucleotide polymorphisms (SNPs) in different ways for the GP of NRs and CL in the Suhuai pig population. The prediction accuracy, bias and running time of 15 different GP models were evaluated by 10-fold cross-validation. The prediction accuracy of GBLUP using chip data for NRs and CL was 0.314 ± 0.022 and 0.194 ± 0.040, respectively. For NRs, based on the iWGS data, treating the most significant SNP as fixed effects in the GBLUP model had the highest predictive performance, with a prediction accuracy of 0.528 ± 0.023. For CL, based on the chip data, the model that added all the significant SNPs identified by imputed data by GWAS into the multi-trait GBLUP as the second random additive effect was the highest predictive performance, with a prediction accuracy of 0.305 ± 0.027. This study provides insights into optimizing GP models for small populations with phenotypes that are difficult to measure.

Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.

Interactions between genes and environmental factors may play a key role in the etiology of many common disorders. Several regularized generalized linear models have been proposed for hierarchical selection of gene by environment interaction effects, where a gene-environment interaction effect is selected only if the corresponding genetic main effect is also selected in the model. However, none of these methods allow to include random effects to account for population structure, subject relatedness and shared environmental exposure. In this article, we develop a unified approach based on regularized penalized quasi-likelihood estimation to perform hierarchical selection of gene-environment interaction effects in sparse regularized mixed models. We compare the selection and prediction accuracy of our proposed model with existing methods through simulations under the presence of population structure and shared environmental exposure. We show that for all simulation scenarios, including and additional random effect to account for the shared environmental exposure reduces the false positive rate and false discovery rate of our proposed method for selection of both gene-environment interaction and main effects. Using the score as a balanced measure of the false discovery rate and true positive rate, we further show that in the hierarchical simulation scenarios, our method outperforms other methods for retrieving important gene-environment interaction effects. Finally, we apply our method to a real data application using the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, and found that our method retrieves previously reported significant loci.

Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression

ABSTRACTSelect measures of social and environmental determinants of health (referred to as “geomarkers”), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center‐level norms. The extent to which hyper‐localization, defined as increasing the spatiotemporal precision of geomarkers, aids in prediction of rapid lung decline remains unclear. Linear mixed effects (LME) models with specialized covariance functions have been used for predicting rapid lung function decline, but there are few options to properly incorporate spatial correlation into the covariance functions while inducing simultaneous variable selection. Our innovative Bayesian model uses a spike and slab prior for simultaneous variable selection and offers additional advantages when coupled with nonstationary Gaussian LME modeling. This model also incorporates spatial correlation through an additional random effect term that accounts for spatial correlation based on ZIP code distances. We validated the model with simulations and applied it to real CF data from a Midwestern CF Center. We demonstrate how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors using Bayesian false discovery rate controlling rule. Our results indicate that incorporating spatiotemporal effects and geomarkers into this novel Bayesian stochastic LME model enhances the dynamic prediction of rapid CF disease progression.

Broiler flocks in production systems with slower-growing breeds and reduced stocking density receive fewer antibiotic treatments and have lower mortality

In the Netherlands, the number of broiler production systems with higher welfare standards, using slower-growing broilers and decreased stocking densities, has increased over the last decade. This study aimed to investigate the effect of this change on antibiotic treatments, mortality, and footpad lesions. Data from national monitoring databases from 2013 to 2021 were used, resulting in 113,380 included flocks from 917 farms. Flocks were divided into conventional (CONV), medium-growing (MED), and slow-growing (SLOW), based on breed and slaughter age (median age: CONV 42 d; MED 50 d; SLOW 56 d). Generalized mixed-effect models were created to compare antibiotic treatments in and after the first week, total on-farm mortality, and footpad lesion scores between these 3 production systems. Year, quarter, flock size, thinning, number of houses, and regional density of poultry farms were included as fixed effects. Random effects were farm and veterinary practice in all models, with an additional random slaughterhouse effect to describe footpad lesions. Probability of treatment in the first week of age in CONV flocks overall years (7.2%, 95% CI [5.9, 8.7]) was higher than in MED (2.0%, 95% CI [1.6, 2.5]) and SLOW flocks (1.3%, 95% CI [1.0, 1.7]). Treatment probability after the first week was similarly higher in CONV flocks (14.7%, 95% CI [12.1, 17.6]) than in MED (3.2%, 95% CI [2.5, 4.0]) and SLOW flocks (2.2%, 95% CI [1.7, 2.9]). CONV flocks had a higher mean mortality (3.2%, 95% CI [3.0, 3.4]) than MED (2.0%, 95% CI [1.9, 2.1]) and SLOW flocks (1.9%, 95% CI [1.8, 2.0]). Regarding footpad lesions, CONV flocks had the highest mean scores (range 0-200) over all years, whereas SLOW flocks had the lowest scores (CONV: 46.1, 95% CI [42.1, 50.6]; MED: 21.3, 95% CI [18.9, 24.0]; SLOW: 13.2, 95% CI [11.5, 15.1]). This analysis of data from flocks over a 9-yr period indicates that switching from conventional to alternative production systems with higher welfare standards could positively affect broiler health and antibiotic use.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Negative variance components and intercept-slope correlations greater than one in magnitude: How do such "non-regular" random intercept and slope models arise, and what should be done when they do?

Statistical models with random intercepts and slopes (RIAS models) are commonly used to analyze longitudinal data. Fitting such models sometimes results in negative estimates of variance components or estimates on parameter space boundaries. This can be an unlucky chance occurrence, but can also occur because certain marginal distributions are mathematically identical to those from RIAS models with negative intercept and/or slope variance components and/or intercept-slope correlations greater than one in magnitude. We term such parameters "pseudo-variances" and "pseudo-correlations," and the models "non-regular." We use eigenvalue theory to explore how and when such non-regular RIAS models arise, showing: (i) A small number of measurements, short follow-up, and large residual variance increase the parameter space for which data (with a positive semidefinite marginal variance-covariance matrix) are compatible with non-regular RIAS models. (ii) Non-regular RIAS models can arise from model misspecification, when non-linearity in fixed effects is ignored or when random effects are omitted. (iii) A non-regular RIAS model can sometimes be interpreted as a regular linear mixed model with one or more additional random effects, which may not be identifiable from the data. (iv) Particular parameterizations of non-regular RIAS models have no generality for all possible numbers of measurements over time. Because of this lack of generality, we conclude that non-regular RIAS models can only be regarded as plausible data-generating mechanisms in some situations. Nevertheless, fitting a non-regular RIAS model can be acceptable, allowing unbiased inference on fixed effects where commonly recommended alternatives such as dropping the random slope result in bias.

Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis niloticus)

This study aimed to verify the possibility of obtaining genetic gain by selecting breeders based on genetic parameter estimates for reproductive performance traits of female tilapia (Oreochromis sp.) from a commercial population. The study was conducted using a database containing information on reproductive traits collected from 2009 to 2021. During the breeding period, the animals were stocked in tanks in a 1:3 male: female ratio. Eggs were collected from the mouth of the females every five days, and the collected eggs were measured in 100 mL beakers to estimate the egg volume. To estimate the variance components and genetic parameters (heritability, correlation, and repeatability), a mixed animal model was used with the fixed effects of the production cycle and breeding tanks, in addition to the additive direct genetic and permanent environmental random effects. The heritability values were 0.26 ± 0.14, 0.18 ± 0.10, 0.15 ± 0.09, and 0.13 ± 0.08 for total egg volume, average egg volume, spawning frequency, and average spawning interval, respectively, with repeatability above 0.43 for all analyzed variables, demonstrating the ability of the evaluated breeds to maintain the same production level during the different cycles. The genetic correlations among the variables were high, with a correlation of −0.99 between the total egg volume and the average spawning interval. Thus, selecting for total egg volume, which had the highest heritability, would result in animals that produce a large volume of eggs and have short spawning intervals.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi etal. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modelingframework.

Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive-definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

140 Targhee Sire Benchmarking at the U.S. Sheep Experiment Station

Abstract The Targhee breed was developed at the U.S. Sheep Experiment Station (USSES) starting in 1926, and has become an established range breed used commercially in the Western U.S. Our objective was to benchmark the performance of industry-based (external) Targhee sires with those currently in use at USSES. Nineteen Targhee sires from 12 flocks were incorporated into the USSES breeding flock starting with the 2015 breeding season. Data were measured through 2022 for 3 production and 12 wool traits with a range of 180 to 1,967 records per trait. Externally sourced sires averaged 38.3 progeny (7 to 78), with 0 to 4 ram lambs (15 total) and 0 to 15 (106 total) ewe lambs retained per sire in the flock. They were ancestors of 984 grand-progeny. USSES sires averaged 25.0 progeny (5 to 52), with 0 to 1 ram lambs (2 total) and 0 to 8 (53 total) ewe lambs retained per sire. They had 275 grand-progeny. Pedigree data was used to compute the marginal genetic contributions for each sire to the current flock using ENDOG. External sires had a cumulative contribution of 31.9 percent (0.4 to 3.9) while the USSES sires contributed 1.4 percent (0.0 to 0.9). Using R, fixed effects examined were age of dam, lamb sex, lamb birth year, grazing group, and type of birth/rearing. Significant variation explained in a trait by main effects (P < 0.005), and their two-way interactions (P < 0.001), were included in the final linear mixed models fitted. Externally sourced and USSES sires were assigned to separate genetic groups to account for the anticipated differences in genetic base. Using WOMBAT, a univariate animal model was fitted to estimate variance components and predict estimated breeding values for each lamb trait. Selection of random effects was based on log-likelihood ratio tests. Final models fitted and estimated heritabilities are summarized in Table 1. Total litter weight at 120 days per ewe lambing (TW120) was evaluated using a repeated measures model with direct additive and permanent environmental random effects of the ewe. Genetic group solutions were not significantly different (P > 0.05) for any traits evaluated. Since both sire groups originated from the same population, this suggests significant selection divergence had not occurred between industry and USSES flocks. Incorporation of external Targhee sires increased the genetic diversity of the USSES flock. It also strengthened industry ties to the USSES flock, which will benefit the commercial genetic evaluation conducted by the National Sheep Improvement Program. USDA is an equal opportunity provider and employer.

Impact of heat on emergency hospital admission in Texas: geographic and racial/ethnic disparities.

Studies exploring the racial/ethnicity disparity of the impact of heat on hospital admission are notably limited, especially in Texas, a state with a diverse population and consistently ranking among the top ten U.S. states for heat-related deaths per capita from 2018 to 2020. Our objective is to determine the correlation between elevated temperatures and emergency hospital admissions for various causes and age groups across 12 Metropolitan Statistical Areas(MSAs) in Texas. Additionally, we aim to investigate health inequalities in the five largest MSAs in Texas between 2004 and 2013. We used MSA-level hospital admission and weather data to estimate the relationship between heat and emergency hospital admissions. We applied a Generalized Additive Model and random effects meta-analysis to calculate MSA-specific associations and overall correlation, repeating the analysis for age groups and specific causes of admission. We also investigated health disparities across racial and ethnic groups and performed a sensitivity analysis. The results showed that a 1 °C increase in temperature was associated with a 0.50% (95% CI [0.38%, 0.63%]) increase in all-cause emergency hospital admissions. Heat's impact on hospital admissions varied among age groups and causes, with children under 6 years showing the highest effect estimate (0.64% (95% CI [0.32%,0.96%])). Statistically significant associations were found for Cardiovascular Diseases (0.27% (95% CI [0.07%,0.47%])), Ischemic Heart Diseases (0.53% (95% CI [0.15%,0.92%])), Pneumonia (0.70% (95% CI [0.25%,1.16%])), and Respiratory Diseases (0.67% (95% CI [0.18%,1.17%])). Health disparities were found among racial and ethnic groups in the five largest MSAs. Studies exploring the impact of heat on hospital admission in Texas are notably limited. Our research provided a comprehensive examination of the connection between heat and emergency hospital admissions throughout Texas. Furthermore, we are the first to examine racial/ethnic disparities, identifying African American and Hispanic groups as disproportionately affected. These insights provide valuable insights for policymakers to allocate resources and implement strategies to mitigate the negative consequences of rising temperatures.

The genetic and phenotypic associations between lamb survival outcomes and other traits recorded at lambing

Context Australian sheep breeding values (ASBVs) for lambing ease (LE) are estimated by Sheep Genetics, by using a threshold animal model at the lamb level, in a tri-variate analysis that includes data on birth weight, gestation length and lambing ease score. The implications of these traits for lamb survival, or the use of other indirect traits to improve accuracy of ASBVs for LE, are not currently being considered. Ultimately, it is desirable to extend the analysis to outcomes for individual lamb survival. Aim The present study investigated implications of LE for lamb survival outcomes, accounting for litter size, and examined associations with other traits recorded at or shortly after lambing in maternal sheep breeds. Methods Equivalent linear models were used to compare lamb- and ewe-level models with various combinations of additional random effects. In particular, lambing ease was treated as a different trait for single-born and twin-born lambs, to identify changes in genetic correlations associated with litter size between LE and other traits. Other traits included lambs recorded dead at birth, survival to weaning, lamb birth weight, gestation length and maternal behaviour score. Key results Individual lamb survival outcomes inferred from field data and dead at birth lambs, are lowly heritable traits influenced by both direct and maternal effects. Lamb survival is positively correlated with birth weight, but negatively correlated with gestation length, lambing ease score (increasing lambing difficulty) and dead at birth lambs. Genetic and phenotypic correlations demonstrated that birth weight and lambing ease are antagonistic traits, more so for single-born lambs. Genetic correlations were moderate between dead at birth lambs and LE (0.40–0.45 singles; 0.15–0.36 including data from twins) or lamb survival (−0.63 to −0.81 singles; −0.00 to −0.23 including data from twins) and can add to the accuracy of genetic evaluation for these traits. In contrast, maternal behaviour score was predominantly an ewe trait, and will therefore add to accuracy of evaluation only for maternal effects. Lamb-level models appeared to underestimate heritability, sometimes compensated for by larger variance, and over-estimate genetic correlations for some traits relative to ewe-level models. Conclusions Expanding the current lambing ease analysis to include dead at birth records and lamb survival outcomes would provide more detailed options for breeders to develop breeding goals to improve outcomes for both ewes and lambs. Further work is required to expand analyses to include threshold and continuous traits and understand genetic contributions to ewe survival traits. Implications Relative selection emphasis on LE and birth weight must be considered in light of the expected litter size in which lambs will be born, to ensure favourable outcomes for lamb survival overall. Accuracy of genetic evaluation for LE can be improved using data on dead at birth. Equivalent ewe model analyses are possible. Completeness of pedigree, availability of informative lamb level data and integration with other traits are also factors to consider for the choice between operational lamb- versus ewe-level models for genetic evaluation systems.

Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.

Vaccinating Meat Chickens against Campylobacter and Salmonella: A Systematic Review and Meta-Analysis.

Foodborne enteritis is a major disease burden globally. Two of the most common causative bacterial enteropathogens in humans are Campylobacter and Salmonella species which are strongly associated with the consumption of raw or contaminated chicken. The poultry industry has approached this issue by use of a multi-hurdle method across the production chain to reduce or eliminate this risk. The use of poultry vaccines is one of these control methods. A systematic review and meta-analysis of vaccination effects against caecal Campylobacter and Salmonella were performed on primary research published between 2009 and 2022. Screening was conducted by three reviewers with one reviewer performing subsequent data extraction and one reviewer performing the risk of bias assessment. The confidence in cumulative evidence was evaluated based on the GRADE method. Meta-analyses were performed using standardised mean differences (SMDs) with additional analyses and random effects regression models on intervention effects grouped by the vaccine type. A total of 13 Campylobacter and 19 Salmonella studies satisfied the eligibility criteria for this review. Many studies included multi-arm interventions, resulting in a total of 25 Campylobacter and 34 Salmonella comparators which were synthesised. The analyses revealed a large reduction in pathogen levels; however, many effects required statistical adjustment due to unit of analysis errors. There was a moderate level of confidence in the reduction of Campylobacter by 0.93 SMD units (95% CI: −1.275 to −0.585; p value < 0.001) and a very low level of confidence in the reduction of Salmonella by 1.10 SMD units (95% CI: −1.419 to −0.776; p value < 0.001). The Chi2 test for heterogeneity (p value 0.001 and <0.001 for Campylobacter and Salmonella, respectively) and the I2 statistic (52.4% and 77.5% for Campylobacter and Salmonella, respectively) indicated high levels of heterogeneity in the SMDs across the comparators. The certainty of gathered evidence was also affected by a high risk of study bias mostly due to a lack of detailed reporting and, additionally for Salmonella, the presence of publication bias. Further research is recommended to source areas of heterogeneity, and a conscious effort to follow reporting guidelines and consider units of analysis can improve the strength of evidence gathered to provide recommendations to the industry.

Multiple imputation approaches for handling incomplete three-level data with time-varying cluster-memberships.

Three‐level data arising from repeated measures on individuals clustered within higher‐level units are common in medical research. A complexity arises when individuals change clusters over time, resulting in a cross‐classified data structure. Missing values in these studies are commonly handled via multiple imputation (MI). If the three‐level, cross‐classified structure is modeled in the analysis, it also needs to be accommodated in the imputation model to ensure valid results. While incomplete three‐level data can be handled using various approaches within MI, the performance of these in the cross‐classified data setting remains unclear. We conducted simulations under a range of scenarios to compare these approaches in the context of an acute‐effects cross‐classified random effects substantive model, which models the time‐varying cluster membership via simple additive random effects. The simulation study was based on a case study in a longitudinal cohort of students clustered within schools. We evaluated methods that ignore the time‐varying cluster memberships by taking the first or most common cluster for each individual; pragmatic extensions of single‐ and two‐level MI approaches within the joint modeling (JM) and the fully conditional specification (FCS) frameworks, using dummy indicators (DI) and/or imputing repeated measures in wide format to account for the cross‐classified structure; and a three‐level FCS MI approach developed specifically for cross‐classified data. Results indicated that the FCS implementations performed well in terms of bias and precision while JM approaches performed poorly. Under both frameworks approaches using the DI extension should be used with caution in the presence of sparse data.

Nonparametric variable screening for multivariate additive models

In this paper we develop a novel procedure of variable screening for a multivariate additive random-effects model, based on B-spline function approximations. With these approximations, the so-called signal-to-noise ratio (SNR) can be defined to inform the importance of each covariate in the model. Then, SNR-based forward filtering is conducted on covariates by using iterative projections of the multiple response data into the space of covariates. The proposed procedure is easy to use and allows the user to pool non-linear information across heterogeneous subjects through random-effects variables. We establish an asymptotic theory on the selection consistency under some regularity conditions. By simulations, we show that the procedure has a superior performance over some existing methods in terms of sensitivity and specificity. We also apply the procedure to anti-cancer drug data, revealing a set of biomarkers that potentially influence concentrations of anti-cancer drugs in cancer cell lines.

The effect of corporate lobbying on fraud and money laundering

PurposeThis study aims to assess the effect of corporate lobbying power on fraud and money laundering in listed firms on the Tehran Stock Exchange. For the study, the information of 173 firms is assessed during 2013–2020, and a total number of 1,384 year-companies are analysed.Design/methodology/approachIn this paper, the Beneish model is used for fraud detection, and the clause of the auditor’s report on money laundering is used for the variable of money laundering. The multivariate regression, Logistic regression, the fixed effects of panel data, additional random effects tests, Hausman, least generalised squares and T + 1 are used by using the Stata Software.FindingsThe obtained results indicate a direct and significant relationship between lobbying and fraud and lobbying and money laundering. Suppose the board members of firms are among the parliament members or the government cabinet (politicians) and/or major shareholders affiliated with state-owned and/or quasi-governmental institutions. In that case, the likelihood of corporate lobbying will be increased.Originality/valueThe outcomes of the current study give great insight to developing countries due to the high volume of money laundering to reduce such a financial crime.

Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle

The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.

PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

Abstract The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

PSXV-9 Transgenerational epigenetic variance for production and reproduction traits in maternal-line pigs

Abstract Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (λ) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal λ value (λ values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal λ value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal λ value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs.